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title: ImgAuth AI
emoji: 🛡️
colorFrom: purple
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
---
# ImgAuth AI — Image Authenticity Detector
🛡️ **ImgAuth AI** is a state-of-the-art web application designed to detect AI-generated and manipulated images. Built with a "simple on the surface, powerful underneath" philosophy, it combines deep learning models with advanced digital forensics heuristics to deliver clear, binary verdicts: **Likely AI-Generated** or **Likely Authentic**.
Developed as a Major Project by **Team VisionGuard** (student team of 4).
---
## 🚀 Key Features
- **Binary Classification**: Simplified verdicts removing ambiguity ("Likely AI-Generated" or "Likely Authentic").
- **Deep Learning Ensemble**: Combined predictions from 3 Hugging Face model pipelines:
- `umm-maybe/AI-image-detector`
- `dima806/ai_vs_real_image_detection`
- `Organika/sdxl-detector`
- **5 Forensic Heuristics**: Multi-layer analysis for technical validation:
1. *Noise Kurtosis Analysis* (checks high-frequency noise distributions)
2. *Deep Feature Inconsistency (DFI)* (checks patch-level consistency of Vision Transformer embeddings)
3. *FFT Spectral Analysis* (identifies periodic artifacts in frequency domain)
4. *Color Histogram Analysis* (detects synthetic pixel roughness/smoothness)
5. *JPEG Ghost Analysis* (detects double compression artifacts in JPEG files)
- **AI Focus Areas (Explainability)**: Visual heatmaps showing ViT Attention Maps and Deep Feature Inconsistencies.
- **Collapsible Technical Drawer**: Advanced forensic signal logs, weights, and metrics available for researchers, while maintaining a clean, technical-jargon-free interface for everyday users.
- **Privacy First**: Fully stateless architecture; no images are stored permanently. Scanning history is saved only in local browser storage (`localStorage`).
---
## 👥 Meet Team VisionGuard
- **Vishal Chauhan** (Computer Science & Engineering, Project Lead)
- **Prince Mishra** (Computer Science & Engineering, Backend Developer)
- **Prince Dubey** (Computer Science & Engineering, Security & Testing)
- **Raksha** (Computer Science & Engineering, Frontend Developer)
---
## 🛠️ Technology Stack
- **Backend**: FastAPI, Uvicorn, PyTorch, Hugging Face Transformers, OpenCV, NumPy, SciPy
- **Frontend**: Vanilla HTML5, CSS3 (Modern dark-theme layout with purple gradients & glassmorphism), Vanilla JavaScript
- **Deployment**: Docker, Hugging Face Spaces
---
## 💻 Local Setup and Running
To run this application locally on your machine, follow these steps:
### Prerequisites
- Python 3.10+
- Pip package manager
### Installation
1. **Clone the repository**:
```bash
git clone <repository-url>
cd imgauth-ai
```
2. **Create and activate a virtual environment**:
- **Windows (PowerShell)**:
```powershell
python -m venv .venv
.\.venv\Scripts\activate
```
- **macOS/Linux**:
```bash
python -m venv .venv
source .venv/bin/activate
```
3. **Install dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run the server**:
```bash
python run.py
```
*The app will start running at:* `http://localhost:5000`
---
## 🐳 Running with Docker
Alternatively, build and run via Docker:
1. **Build the image**:
```bash
docker build -t imgauth-ai .
```
2. **Run the container**:
```bash
docker run -p 7860:7860 imgauth-ai
```
*Open browser to:* `http://localhost:7860`
---
## ⚖️ License & Attribution
- **Non-Commercial**: This project uses the `Organika/sdxl-detector` model, licensed under CC BY-NC 4.0. It is intended strictly for non-commercial educational and research purposes.
- **Model Attribution**: All deep learning classifications are handled by model weights published by the Hugging Face community.
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